Correction de l'approximation de Kirchhoff par la méthode intégrale reformulée : cas des réflectivités de surfaces sinusoïdales
Bibliographic record
Abstract
Using two different methods, we study the radiative properties of rough surfaces, such as the bidirectional or the hemispheric directional reflectivity. The first method, which we call exact, is the integral method (MI). It is based on the electromagnetic theory and Green's theorem to describe the system through a system of equations for the field and its normal derivative (sources) at the surface. The method is computation expensive, requiring the inversion of possibly large complex matrices. The second method (MIR), which we will use and for which we extend the validity to include transverse polarization, reformulates the integral method to solve it by an iterative approach. It has the advantage that its first iteration corresponds to the Kirchoff approximation (AK). The following (higher order) terms bring corrections to AK, while reducing notably the computation load. Our main purpose is to study the stability of the MIR and to find the limits of its validity when compared with the (exact) MI. Our numerical results were carried out for perfectly conducting or dielectric surfaces with sinusoidal roughness for two polarizations, transverse electric and transverse magnetic. [Journal translation]
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".